Background: Although numerous genotype-based warfarin dosing algorithms have been published, there is little data comparing the predictive ability of these algorithms in real clinical practice.
Objectives: Our goal was to evaluate the performance of pharmacogenetic algorithms in an unselected patient population initiating warfarin treatment for atrial fibrillation or valve disease in a real-world clinical setting. The principal objective of the analysis was to determine if Gage’s, Michaud’s, and IWPC algorithms could predict the dose achieving the therapeutic International normalized ratio (INR).
Methods: Data from a retrospective cohort study of 605 patients initiating warfarin therapy at the Montreal Heart Institute was used. We compared the dose predicted by the algorithms to the dose achieving the therapeutic INR. Pearson’s correlation coefficient and mean absolute error (MAE) were used to evaluate the predictive accuracy of the algorithms. Clinical accuracy of the predictions was assessed by computing the proportion of patients in which the predicted dose was under-estimated, ideally estimated, or overestimated. Finally, we used multiple linear regression analysis to evaluate the accuracy of a predictive model obtained by adding additional covariables in predicting therapeutic warfarin doses.
Results: The proportion of variation explained (adjusted R2) was the highest for Gage’s algorithm (R2 = 44 %) and the mean absolute error was the smallest for the predictions made by Gage’s algorithm (MAE = 1.41 ± 0.06). Moreover, when we compared the proportion of patients whose predicted doses are within ± 20 % of the observed stable dose, Gage’s algorithm also performed the best overall.
Conclusion: The algorithm published by Gage et al. in 2008 is the most accurate pharmacogenetically based equation in predicting therapeutic warfarin dose in our study population.